A video retrieval algorithm based on ensemble similarity

This paper proposed an ensemble similarity based method for video retrieval. An ensemble similarity is used to calibrate the similarity between user given query video clip and each video clip in the database: a clip can be treated as an ensemble which consists of a sequence of multiple key frames. By kernel method, in a high dimension space the feature vector represented frames can be assumed to distribute a Gaussian model. Then probabilistic distance between two Gaussians is computed as the similarity value between two video clips. Then video clips in database with the highest similarity are output and submitted to the user. To improve the speed efficiency, an improved algorithm of Chernoff distance and KL divergence is also proposed. The experimental results indicate that the proposed approach achieves superior performance than some existing methods.